
"""simple docstring"""


import math

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_    							=					logging.get_logger(__name__)

A_    							=					{
    'facebook/data2vec-base-960h': 'https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json',
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}




class  __lowerCamelCase  ( _A			):
				a__:      Union[str, Any]          =    'data2vec-audio'




				def __init__(				self							,						UpperCAmelCase=32							,						UpperCAmelCase=768							,						UpperCAmelCase=12							,						UpperCAmelCase=12							,						UpperCAmelCase=3072							,						UpperCAmelCase="gelu"							,						UpperCAmelCase=0.1							,						UpperCAmelCase=0.1							,						UpperCAmelCase=0.1							,						UpperCAmelCase=0.0							,						UpperCAmelCase=0.1							,						UpperCAmelCase=0.1							,						UpperCAmelCase=0.0_2							,						UpperCAmelCase=1e-5							,						UpperCAmelCase="gelu"							,						UpperCAmelCase=(512, 512, 512, 512, 512, 512, 512)							,						UpperCAmelCase=(5, 2, 2, 2, 2, 2, 2)							,						UpperCAmelCase=(10, 3, 3, 3, 3, 2, 2)							,						UpperCAmelCase=False							,						UpperCAmelCase=16							,						UpperCAmelCase=19							,						UpperCAmelCase=5							,						UpperCAmelCase=0.0_5							,						UpperCAmelCase=10							,						UpperCAmelCase=2							,						UpperCAmelCase=0.0							,						UpperCAmelCase=10							,						UpperCAmelCase=0							,						UpperCAmelCase="sum"							,						UpperCAmelCase=False							,						UpperCAmelCase=False							,						UpperCAmelCase=256							,						UpperCAmelCase=(512, 512, 512, 512, 1500)							,						UpperCAmelCase=(5, 3, 3, 1, 1)							,						UpperCAmelCase=(1, 2, 3, 1, 1)							,						UpperCAmelCase=512							,						UpperCAmelCase=0							,						UpperCAmelCase=1							,						UpperCAmelCase=2							,						UpperCAmelCase=False							,						UpperCAmelCase=3							,						UpperCAmelCase=2							,						UpperCAmelCase=3							,						UpperCAmelCase=None							,						**UpperCAmelCase							,						):
							super().__init__(**UpperCamelCase__							,						pad_token_id=UpperCamelCase__							,						bos_token_id=UpperCamelCase__							,						eos_token_id=UpperCamelCase__   )
							lowerCamelCase_					=  hidden_size
							lowerCamelCase_					=  feat_extract_activation
							lowerCamelCase_					=  list(UpperCamelCase__   )
							lowerCamelCase_					=  list(UpperCamelCase__   )
							lowerCamelCase_					=  list(UpperCamelCase__   )
							lowerCamelCase_					=  conv_bias
							lowerCamelCase_					=  num_conv_pos_embeddings
							lowerCamelCase_					=  num_conv_pos_embedding_groups
							lowerCamelCase_					=  conv_pos_kernel_size
							lowerCamelCase_					=  len(self.conv_dim   )
							lowerCamelCase_					=  num_hidden_layers
							lowerCamelCase_					=  intermediate_size
							lowerCamelCase_					=  hidden_act
							lowerCamelCase_					=  num_attention_heads
							lowerCamelCase_					=  hidden_dropout
							lowerCamelCase_					=  attention_dropout
							lowerCamelCase_					=  activation_dropout
							lowerCamelCase_					=  feat_proj_dropout
							lowerCamelCase_					=  final_dropout
							lowerCamelCase_					=  layerdrop
							lowerCamelCase_					=  layer_norm_eps
							lowerCamelCase_					=  initializer_range
							lowerCamelCase_					=  vocab_size
							lowerCamelCase_					=  use_weighted_layer_sum

							if (
							    (len(self.conv_stride   ) != self.num_feat_extract_layers)
							    or (len(self.conv_kernel   ) != self.num_feat_extract_layers)
							    or (len(self.conv_dim   ) != self.num_feat_extract_layers)
							):
										raise ValueError(
										    '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
										    ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
										    f" {len(self.conv_dim   )}`, `len(config.conv_stride) = {len(self.conv_stride   )}`,"
										    f" `len(config.conv_kernel) = {len(self.conv_kernel   )}`."   )

							# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
							lowerCamelCase_					=  mask_time_prob
							lowerCamelCase_					=  mask_time_length
							lowerCamelCase_					=  mask_time_min_masks
							lowerCamelCase_					=  mask_feature_prob
							lowerCamelCase_					=  mask_feature_length
							lowerCamelCase_					=  mask_feature_min_masks

							# ctc loss
							lowerCamelCase_					=  ctc_loss_reduction
							lowerCamelCase_					=  ctc_zero_infinity

							# adapter
							lowerCamelCase_					=  add_adapter
							lowerCamelCase_					=  adapter_kernel_size
							lowerCamelCase_					=  adapter_stride
							lowerCamelCase_					=  num_adapter_layers
							lowerCamelCase_					=  output_hidden_size or hidden_size

							# SequenceClassification-specific parameter. Feel free to ignore for other classes.
							lowerCamelCase_					=  classifier_proj_size

							# XVector-specific parameters. Feel free to ignore for other classes.
							lowerCamelCase_					=  list(UpperCamelCase__   )
							lowerCamelCase_					=  list(UpperCamelCase__   )
							lowerCamelCase_					=  list(UpperCamelCase__   )
							lowerCamelCase_					=  xvector_output_dim





				@property
				def        UpperCAmelCase__						(				self   ):
							return math.prod(self.conv_stride   )





from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors

def a__       (			A_   ):






  '''simple docstring'''





  __magic_name__								=			prime_factors(A_   )
  if is_square_free(A_   ):
    return -1 if len(A_   ) % 2 else 1
  return 0


if __name__ == "__main__":
    import doctest

    doctest.testmod()

